Wireless　sensor　networks　for　environment　monitoring　are　usually　deployed　in　the　fields　where　electric　or　manual　intervention　cannot　be　accessed　easily.　Therefore,　we　hope　to　minimize　the　times　of　sampling　to　reduce　energy　consuming.　Energy-efficient　sampling　scheduling　can　be　realized　using　compressive　sensing　theory　on　the　basis　of　temporal　correlation　of　the　physical　process.　However,　the　degree　of　correlation　of　neighboring　data　varies　over　time,　which　may　lead　to　different　reconstructive　quality　for　different　parts　of　data　if　constant　duty　cycle　is　used.　We　proposed　SDDC,　a　segmental　dynamic　duty　cycle　control　method,　for　sampling　scheduling　in　wireless　sensor　networks　based　on　compressive　sensing.　Using　a　priori　knowledge　obtained　by　means　of　analysis　on　earlier　sensing　data,　dynamic　duty　cycle　is　determined　according　to　the　linear　degree　of　data　in　each　segment.　The　experimental　results　using　data　from　soil　respiration　monitoring　sensor　networks　show　that　the　proposed　SDDC　method　can　lead　to　better　reconstructive　quality　compared　to　constant　duty　cycle　of　the　same　average　sampling　rate.　That　is　to　say,　the　SDDC　method　needs　smaller　sampling　rate　if　the　reconstructive　error　threshold　is　given　and　consequently　saves　more　energy.